Spatial structure of microbial biomass and activity in prairie soil ecosystems

Katsalirou, Eirini; Deng, Shiping; Nofziger, D. L.; Gerakis, Argyrios; Fuhlendorf, Samuel D.

doi:10.1016/j.ejsobi.2010.04.005

Cited by 33 publications

(21 citation statements)

References 42 publications

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“…Prior to the fire, microbial community abundance in the forest floor was most closely (positively) related to forest floor N concentration, while that in the mineral soil was positively associated with concentrations of C and N (presumably reflecting organic matter content). Relationships between soil organic matter or C content and microbial community structure have been reported in forest soils (Yang et al, 2010;Brockett et al, 2012), grassland soils (Grayston et al, 2004;Katsalirou et al, 2010) and agricultural soils (Yao et al, 2006;Franklin and Mills, 2009). Declines in C content with depth have also been related to changes in soil microbial communities in forest soils (Leckie, 2005;Grayston and Prescott, 2005).…”

Section: Discussionmentioning

confidence: 91%

Changes in soil chemical and biological properties after thinning and prescribed fire for ecosystem restoration in a Rocky Mountain Douglas-fir forest

Switzer

Hope

Grayston

et al. 2012

Forest Ecology and Management

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Section: Discussionmentioning

confidence: 91%

Changes in soil chemical and biological properties after thinning and prescribed fire for ecosystem restoration in a Rocky Mountain Douglas-fir forest

Switzer

Hope

Grayston

et al. 2012

Forest Ecology and Management

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“…Research has shown that in agricultural soils, microbial biomass and respiration are directly related to spatial patterns in soil pH, dissolved organic carbon, and total organic carbon [4]. In contrast, Katsalirou et al [15] found weak spatial structure of organic carbon in cultivated soil, and strong positive linear relationships between soil organic carbon and microbial properties in uncultivated soils. Together these studies demonstrate the limitations of traditional geostatistics for soil sampling designs, and the need to use alternative parameters to guide sampling designs.…”

Section: Introductionmentioning

confidence: 99%

Differences in soil biological activity by terrain types at the sub-field scale in central Iowa US

et al. 2017

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Soil microbial communities are structured by biogeochemical processes that occur at many different spatial scales, which makes soil sampling difficult. Because soil microbial communities are important in nutrient cycling and soil fertility, it is important to understand how microbial communities function within the heterogeneous soil landscape. In this study, a self-organizing map was used to determine whether landscape data can be used to characterize the distribution of microbial biomass and activity in order to provide an improved understanding of soil microbial community function. Points within a row crop field in south-central Iowa were clustered via a self-organizing map using six landscape properties into three separate landscape clusters. Twelve sampling locations per cluster were chosen for a total of 36 locations. After the soil samples were collected, the samples were then analysed for various metabolic indicators, such as nitrogen and carbon mineralization, extractable organic carbon, microbial biomass, etc. It was found that sampling locations located in the potholes and toe slope positions had significantly greater microbial biomass nitrogen and carbon, total carbon, total nitrogen and extractable organic carbon than the other two landscape position clusters, while locations located on the upslope did not differ significantly from the other landscape clusters. However, factors such as nitrate, ammonia, and nitrogen and carbon mineralization did not differ significantly across the landscape. Overall, this research demonstrates the effectiveness of a terrain-based clustering method for guiding soil sampling of microbial communities.

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“…3). This standardization facilitates comparison between different variables (Katsalirou et al, 2010), and helps to avoid that data values may increase or decrease in order of magnitude along Box-Cox transformation. Semivariance (γ(h)) was calculated according to:…”

Section: Fig 3 Data Processingmentioning

confidence: 99%

Meta-analysis of field scale spatial variability of grassland soil CO2 efflux: Interaction of biotic and abiotic drivers

Fóti

Balogh

Herbst

et al. 2016

CATENA

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Abbreviations a -autocorrelation length, c -structural semivariance, DOY -day of year, DOYn -normalized day of year, ME -NashSutcliffe model efficiency coefficient, PCA -principal component analysis, psill -partial sill, R s -soil CO 2 efflux, sdstandard deviation, SSErr -residual sum of squares, SWC -soil water content, SWCn -normalized soil water content, TOCtotal organic carbon content, T s -soil temperature, y0 -nugget variance AbstractIn this study eight temperate grassland sites were monitored for soil CO 2 efflux (R s ) and the spatial covariates soil water content (SWC) and soil temperature (T s ) at fine scale in over 77 measurement campaigns. The goals of this multisite study were to explore the correlations between environmental gradients and spatial patterns of R s , SWC and T s , which are not site-specific and to quantify the relevance of biotic and abiotic controls over spatial patterns along increasing vegetation structural complexity. These patterns in water-limited ecosystems in East-Central Europe are likely to be influenced by summer droughts caused by the changing climate.A consistent experimental setup was applied at the study sites including 75 sampling locations along 15 m circular transects. Spatial data processing was mainly based on variography. Two proxy variables were introduced to relate the site characteristics in terms of soils, water status and vegetation. Normalized SWC (SWCn) reconciled site-specific soil water regimes while normalized day of year integrated temperature and vegetation phenology.A principal component analysis revealed that the progressing closure of vegetation in combination with large R s and SWCn values, as well as low T s and R s variability support the detectability of spatial patterns found in both the abiotic and biotic variables. Our results showed that apart from SWC the pattern of soil temperature also had an effect on spatial structures. We detected that when the spatially 2 structured variability of T s was low, a strong negative correlation existed between SWCn and the spatial autocorrelation length of R s with r=0.66 (p<0.001). However, for high spatially structured variability of T s , occurring presumably at low T s in spring and autumn, the correlation did not exist and it was difficult to quantify the spatial autocorrelation of R s . Our results are indicative of a potential shift from homogeneity and dominance of biotic processes to an increased heterogeneity and abiotic regulation in drought prone ecosystems under conditions of decreasing soil moisture.

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Spatial structure of microbial biomass and activity in prairie soil ecosystems

Cited by 33 publications

References 42 publications

Changes in soil chemical and biological properties after thinning and prescribed fire for ecosystem restoration in a Rocky Mountain Douglas-fir forest

Changes in soil chemical and biological properties after thinning and prescribed fire for ecosystem restoration in a Rocky Mountain Douglas-fir forest

Differences in soil biological activity by terrain types at the sub-field scale in central Iowa US

Meta-analysis of field scale spatial variability of grassland soil CO2 efflux: Interaction of biotic and abiotic drivers

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